It is known to provide a graphical annotation or segmentation of a medical image of a patient by registering a medical atlas with the medical image. For example, a medical atlas may comprise a geometrical description of organs, e.g., of their bounding contours, for a particular field of view (FOV). However, medical images can vary significantly across patients due to patients having organs of different shapes and sizes. As such, registering a medical atlas to a medical image is considered a non-trivial task.
Mathematically, medical atlas registration may be described as an optimization problem, where a set of parameter values of a model need to be determined that define a transformation, or set of transformations, of the medical atlas that provides the best registration to the medical image according to a match function. It has been recognized that one of the core challenges in such medical atlas registration comes from the fact that for most match functions, only perfect alignment between the medical atlas and the medical image produces a perfect match. Incremental improvements may be misleading. Namely, the optimization problem is generally not convex. As such, gradient descent optimizers are likely to get “trapped” in a local minimum. At the same time, evaluating all possible (sequences of individual) transformations is not feasible as it is a mathematically intractable problem.
A possible solution to the above problem is described in the paper “Region segmentation in the frequency domain applied to upper airway real-time magnetic resonance images” by Erik Bresch et al., Medical Imaging, IEEE Transactions on 28.3 (2009): 323-338. Herein, a method of atlas registration for a midsagittal MRI scan of the human vocal tract is described which employs a hierarchy of restricted transformations of the atlas. Hereby, rough large scale alignments are carried out first until no match fit can be achieved, and only then a more fine-grained warping is carried out. In the paper, a series of four distinct, less and less restricted warping operations is employed. This temporary restriction of the transformation space is said to lead to a smoothing of the energy landscape of the optimization problem, which in turn alleviates the risk of getting “trapped” in a local minimum.
However, the solution proposed in the above paper is designed only for this particular atlas, with heuristic components that are only empirically justified. Applying similar approaches to different atlas matching problems is a slow and cumbersome task.
A master thesis titled “Uncertainty in Probabilistic Image Registration” by Tayebeh Lotfi Mahyari describes in section 3.5 using reinforcement learning to guide an iterative image registration procedure. Furthermore, a publication titled “Improving Probabilistic Image Registration via Reinforcement Learning and Uncertainty Evaluation” by Tayebeh Lofti et al. describes a framework for probabilistic image registration which involves assigning probability distributions over spatial transformations.
Technical background on deep neural networks may be obtained from “Deep neural networks for anatomical brain segmentation” by De Brebisson Alexandre et al, 7 Jun. 2015. Technical background on auto encoders may be obtained from “Multi-modal Brain Tumor Segmentation Using Stacked Denoising Autoencoders” by Vaidhya Kiran et al, 5 Oct. 2015. Technical background on (deep) reinforcement learning may be obtained from “Human-level control through deep reinforcement learning” in Nature, 26 Feb. 2015 and from the tutorial “Reinforcement Learning: A Tutorial Scope of Tutorial”, 1 Jan. 1996.